DOI Number:10.15680/IJIRCCE.2025.1301146
Title of Paper:PCB Defect Detection using Deep Learning and Synthetic Data Generation with ControlNet
Journal:IJIRCCE
Key Words:PCB Defect Detection; Synthetic Data Generation; Swin Transformer; ControlNet
Abstract:This defect detection in printed circuit boards (PCBs) is crucial to ensure reliability and functionality of
the equipment used in all Industries. Based on the analysis, this paper develops a new PCB defect detector composed of
Swin Transformer and synthetic data generation based on ControlNet. Lack of availability of data and expensive
labeling process makes the traditional method of defect identification unproductive. To overcome these challenges, this
study uses ControlNet to create relevant synthetic defect samples that enrich the dataset and enhance generalization.
Experimental findings show high increase in performance from 87.5% to 93.4% in accuracy; 85.2% to 91.8% in
precision and 83.6% to 91.2% in F1-score by integrating synthetic data. The proposed approach had better performance
than baseline models, including CNN and YOLOv5, especially in detecting complex and rare defects. The findings of
this research also serve the purpose of enhancing the reliability and efficiency of PCB inspection to reveal the
effectiveness of using more sophisticated methods of data augmentation and transformer-based models in industrial
problems.
First Author:文学志
Indexed by:Journal paper
Correspondence Author:Zamadder Md Iqbal Hossain
Volume:13
Issue:1
Translation or Not:no
Date of Publication:2025-01-19